Literature DB >> 27008014

Meta-Analysis in Gene Expression Studies.

Levi Waldron1, Markus Riester2.   

Abstract

This chapter introduces methods to synthesize experimental results from independent high-throughput genomic experiments, with a focus on adaptation of traditional methods from systematic review of clinical trials and epidemiological studies. First, it reviews methods for identifying, acquiring, and preparing individual patient data for meta-analysis. It then reviews methodology for synthesizing results across studies and assessing heterogeneity, first through outlining of methods and then through a step-by-step case study in identifying genes associated with survival in high-grade serous ovarian cancer.

Entities:  

Keywords:  Biomarkers; Computational molecular biology; Gene expression profiling; Meta-analysis; Microarray analysis; Ovarian neoplasms

Mesh:

Year:  2016        PMID: 27008014     DOI: 10.1007/978-1-4939-3578-9_8

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  9 in total

1.  A systematic review of datasets that can help elucidate relationships among gene expression, race, and immunohistochemistry-defined subtypes in breast cancer.

Authors:  Ifeanyichukwu O Nwosu; Stephen R Piccolo
Journal:  Cancer Biol Ther       Date:  2021-08-19       Impact factor: 4.875

2.  Cancer Transcriptome Dataset Analysis: Comparing Methods of Pathway and Gene Regulatory Network-Based Cluster Identification.

Authors:  Seungyoon Nam
Journal:  OMICS       Date:  2017-04

Review 3.  Available Software for Meta-analyses of Genome-wide Expression Studies.

Authors:  Diego A Forero
Journal:  Curr Genomics       Date:  2019-08       Impact factor: 2.236

4.  Meta-Analysis of Vaginal Microbiome Data Provides New Insights Into Preterm Birth.

Authors:  Idit Kosti; Svetlana Lyalina; Katherine S Pollard; Atul J Butte; Marina Sirota
Journal:  Front Microbiol       Date:  2020-04-08       Impact factor: 5.640

5.  Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach.

Authors:  Karla Cervantes-Gracia; Richard Chahwan; Holger Husi
Journal:  Front Genet       Date:  2022-02-04       Impact factor: 4.599

6.  Meta-analysis highlights the key drought responsive genes in genes: PEPC and TaSAG7 are hubs response networks.

Authors:  Sahar Shojaee; Rudabeh Ravash; Behrouz Shiran; Esmaeil Ebrahimie
Journal:  J Genet Eng Biotechnol       Date:  2022-09-02

Review 7.  Understanding the Molecular Mechanisms of Asthma through Transcriptomics.

Authors:  Heung Woo Park; Scott T Weiss
Journal:  Allergy Asthma Immunol Res       Date:  2020-05       Impact factor: 5.764

8.  A novel estimator of between-study variance in random-effects models.

Authors:  Nan Wang; Jun Zhang; Li Xu; Jing Qi; Beibei Liu; Yiyang Tang; Yinan Jiang; Liang Cheng; Qinghua Jiang; Xunbo Yin; Shuilin Jin
Journal:  BMC Genomics       Date:  2020-02-11       Impact factor: 3.969

Review 9.  Multi-Omics Profiling Approach to Asthma: An Evolving Paradigm.

Authors:  Yadu Gautam; Elisabet Johansson; Tesfaye B Mersha
Journal:  J Pers Med       Date:  2022-01-07
  9 in total

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